Methods to assess changes in human brain structure across the lifecourse

Human brain structure can be measured across the lifecourse (“in vivo”) with magnetic resonance imaging (MRI). MRI data are often used to create “atlases” and statistical models of brain structure across the lifecourse. These methods may define how brain structure changes through life and support diagnoses of increasingly common, yet still fatal, age-related neurodegenerative diseases. As diseases such as Alzheimer’s (AD) cast an ever growing shadow over our ageing population, it is vitally important to robustly define changes which are normal for age and those which are pathological. This work therefore assessed existing MR brain image data, atlases, and statistical models. These assessments led me to propose novel methods for accurately defining the distributions and boundaries of normal ageing and pathological brain structure. A systematic review found that there were fewer than 100 appropriately tested normal subjects aged ≥60 years openly available worldwide. These subjects did not have the range of MRI sequences required to effectively characterise the features of brain ageing. The majority of brain image atlases identified in this review were found to contain data from few or no subjects aged ≥60 years and were in a limited range of MRI sequences. All of these atlases were created with parametric (mean-based) statistics that require the assumptions of equal variance and Gaussian distributions. When these assumptions are not met, mean-based atlases and models may not well represent the distributions and boundaries of brain structure. I tested these assumptions and found that they were not met in whole brain, subregional, and voxel-based models of ~580 subjects from across the lifecourse (0- 90 years). I then implemented novel whole brain, subregional, and voxel-based statistics, e.g. percentile rank atlases and nonparametric effect size estimates. The equivalent parametric statistics led to errors in classification and inflated effects by up to 45% in normal ageing-AD comparisons. I conclude that more MR brain image data, age appropriate atlases, and nonparametric statistical models are needed to define the true limits of normal brain structure. Accurate definition of these limits will ultimately improve diagnoses, treatment, and outcome of neurodegenerative disease

GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks

One of the biggest issues facing the use of machine learning in medical imaging is the lack of availability of large, labelled datasets. The annotation of medical images is not only expensive and time consuming but also highly dependent on the availability of expert observers. The limited amount of training data can inhibit the performance of supervised machine learning algorithms which often need very large quantities of data on which to train to avoid overfitting. So far, much effort has been directed at extracting as much information as possible from what data is available. Generative Adversarial Networks (GANs) offer a novel way to unlock additional information from a dataset by generating synthetic samples with the appearance of real images. This paper demonstrates the feasibility of introducing GAN derived synthetic data to the training datasets in two brain segmentation tasks, leading to improvements in Dice Similarity Coefficient (DSC) of between 1 and 5 percentage points under different conditions, with the strongest effects seen fewer than ten training image stacks are available

An exploratory study of predictors of response to vagus nerve stimulation paired with upper-limb rehabilitation after ischemic stroke

We have previously shown the safety and feasibility of vagus nerve stimulation (VNS) paired with upper-limb rehabilitation after ischemic stroke. In this exploratory study, we assessed whether clinical and brain MRI variables predict response to treatment. We used data from two completed randomised and blinded clinical trials (N = 35). All participants had moderate to severe upper-limb weakness and were randomised to 6-weeks intensive physiotherapy with or without VNS. Participants had 3 T brain MRI at baseline. The primary outcome was change in Fugl-Meyer Assessment, upper-extremity score (FMA-UE) from baseline to the first day after therapy completion. We used general linear regression to identify clinical and brain MRI predictors of change in FMA-UE. VNS-treated participants had greater improvement in FMA-UE at day-1 post therapy than controls (8.63 ± 5.02 versus 3.79 ± 5.04 points, t = 2.83, Cohen’s d = 0.96, P = 0.008). Higher cerebrospinal fluid volume was associated with less improvement in FMA-UE in the control but not VNS group. This was also true for white matter hyperintensity volume but not after removal of an outlying participant from the control group. Responders in the VNS group had more severe arm impairment at baseline than responders to control. A phase III trial is now underway to formally determine whether VNS improves outcomes and will explore whether these differ in people with more severe baseline upper-limb disability and cerebrovascular disease

Stroke aetiological classification reliability and effect on trial sample size: systematic review, meta-analysis and statistical modelling

Background: Inter-observer variability in stroke aetiological classification may have an effect on trial power and estimation of treatment effect. We modelled the effect of misclassification on required sample size in a hypothetical cardioembolic (CE) stroke trial. Methods: We performed a systematic review to quantify the reliability (inter-observer variability) of various stroke aetiological classification systems. We then modelled the effect of this misclassification in a hypothetical trial of anticoagulant in CE stroke contaminated by patients with non-cardioembolic (non-CE) stroke aetiology. Rates of misclassification were based on the summary reliability estimates from our systematic review. We randomly sampled data from previous acute trials in CE and non-CE participants, using the Virtual International Stroke Trials Archive. We used bootstrapping to model the effect of varying misclassification rates on sample size required to detect a between-group treatment effect across 5000 permutations. We described outcomes in terms of survival and stroke recurrence censored at 90 days. Results: From 4655 titles, we found 14 articles describing three stroke classification systems. The inter-observer reliability of the classification systems varied from ‘fair’ to ‘very good’ and suggested misclassification rates of 5% and 20% for our modelling. The hypothetical trial, with 80% power and alpha 0.05, was able to show a difference in survival between anticoagulant and antiplatelet in CE with a sample size of 198 in both trial arms. Contamination of both arms with 5% misclassified participants inflated the required sample size to 237 and with 20% misclassification inflated the required sample size to 352, for equivalent trial power. For an outcome of stroke recurrence using the same data, base-case estimated sample size for 80% power and alpha 0.05 was n = 502 in each arm, increasing to 605 at 5% contamination and 973 at 20% contamination. Conclusions: Stroke aetiological classification systems suffer from inter-observer variability, and the resulting misclassification may limit trial power. Trial registration: Protocol available at reviewregistry540

Mediterranean-type diet and brain structural change from 73 to 76 years in a Scottish cohort

STUDY FUNDING The data were collected by a Research into Ageing programme grant; research continues as part of the Age UK–funded Disconnected Mind project. The work was undertaken by The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross-council Lifelong Health and Wellbeing Initiative (MR/K026992/1), with funding from the BBSRC and Medical Research Council. Imaging and image analysis was performed at the Brain Research Imaging Centre (sbirc.ed.ac.uk/), Edinburgh, supported by the Scottish Funding Council SINAPSE Collaboration. Derivation of mean cortical thickness measures was funded by the Scottish Funding Council’s Postdoctoral and Early Career Researchers Exchange Fund awarded by SINAPSE to David Alexander Dickie. L.C.A.C. acknowledges funding from the Scottish Government's Rural and Environment Science and Analytical Services (RESAS) division.Peer reviewedPublisher PD

Vagus nerve stimulation paired with upper limb rehabilitation after chronic stroke

Background and Purpose: We assessed safety, feasibility, and potential effects of vagus nerve stimulation (VNS) paired with rehabilitation for improving arm function after chronic stroke. Methods: We performed a randomized, multisite, double-blinded, sham-controlled pilot study. All participants were implanted with a VNS device and received 6-week in-clinic rehabilitation followed by a home exercise program. Randomization was to active VNS (n=8) or control VNS (n=9) paired with rehabilitation. Outcomes were assessed at days 1, 30, and 90 post-completion of in-clinic therapy. Results: All participants completed the course of therapy. There were 3 serious adverse events related to surgery. Average FMA-UE scores increased 7.6 with active VNS and 5.3 points with control at day 1 post–in-clinic therapy (difference, 2.3 points; CI, −1.8 to 6.4; P=0.20). At day 90, mean scores increased 9.5 points from baseline with active VNS, and the control scores improved by 3.8 (difference, 5.7 points; CI, −1.4 to 11.5; P=0.055). The clinically meaningful response rate of FMA-UE at day 90 was 88% with active VNS and 33% with control VNS (P<0.05). Conclusions: VNS paired with rehabilitation was acceptably safe and feasible in participants with upper limb motor deficit after chronic ischemic stroke. A pivotal study of this therapy is justified